Erin Mordecai NIGMS R35 ESI MIRA Summary Leveraging environmental drivers to predict vector-borne disease transmission Vector-borne diseases are an increasingly urgent public health crisis worldwide. Traditional biomedical approaches such as vaccines and drugs alone will not sustainably control vector-borne diseases or prevent future emergence. More proactive, ecological approaches that discover and disrupt the environmental drivers of vector transmission are critical for understanding and sustainably controlling disease epidemics. Predicting infectious disease dynamics from ecological drivers like climate and land use is appealing because these drivers are readily observable and often predictable, and their impacts on disease transmission are supported by mechanistic hypotheses. However, vector-borne diseases, like other ecological systems, are nonlinear, complex, and dynamic, making prediction challenging in a stochastic and changing world. My research uses brings in techniques from quantitative ecology, statistics, mathematics, econometrics, and geography as well as newly available data sources to understand and predict vector-borne disease dynamics in response to global change. Our preliminary work has shown that climate and land use are powerful predictors of geographical and seasonal patterns of disease transmission. I now propose to extend this work to understand disease dynamics using cutting edge quantitative techniques and time series data. Specifically, we will investigate how climate, habitat, behavior, and immunity interact to determine disease dynamics over space and time for malaria, Zika, dengue, and other vector-borne pathogens, building a portfolio of evidence and predictive tools from multiple complementary quantitative approaches. These include fitting increasingly sophisticated dynamic models to time series data, applying empirical dynamic modeling to infer, rather than assume, mechanistic relationships with ecological drivers, and applying econometric panel analysis to remotely sensed and geographic data to evaluate evidence for bidirectional causation between disease and human land use activities. Recent decades have witnessed both unprecedented expansions in both vector-borne disease and technological and computational capacity. In response, vector-borne disease modeling research is rapidly accelerating, with the goal of improving prospective prediction and thereby opening opportunities for proactive control. By developing and testing new theory, this project will finally allow us to leverage environmental drivers of vector-borne disease to understand the mechanisms underlying complex disease dynamics, and to predict future disease risk in changing environments.